This project provides a local, on-device alternative to GitHub Copilot for Macbooks, targeting developers who need code completion capabilities without relying on cloud services, especially in environments with poor network connectivity. It offers a one-click solution for local AI-powered coding assistance.

How It Works

LocalPilot leverages local LLMs, specifically GGML-quantized models, to provide code completion. It integrates with VS Code by proxying requests to a local Python server running these models. The advantage is offline functionality and potential privacy benefits, though performance and completion quality are model-dependent and can be less sophisticated than cloud-based services for complex tasks.

Quick Start & Requirements

• Install dependencies: virtualenv venv && source venv/bin/activate && pip install -r requirements.txt
• Setup (downloads models): python app.py --setup
• Run: python app.py
• Requires Python 3.x, VS Code with the GitHub Copilot extension configured to use the local proxy. Model downloads can be substantial.

Highlighted Details

• Aims to replicate GitHub Copilot functionality locally.
• Performance is comparable to cloud Copilot for smaller (7b) models on Apple Silicon (M2 Max), but slower for larger (34b) models.
• Explicitly stated as a demonstration of an inefficient implementation, encouraging community contributions for optimization.
• Primarily beneficial for users with poor internet connectivity or during travel.

Maintenance & Community

The project is maintained by danielgross. There are no explicit mentions of community channels, roadmap, or other contributors in the README.

Licensing & Compatibility

The README does not specify a license. This lack of explicit licensing makes commercial use or integration into closed-source projects uncertain and potentially restricted.

Limitations & Caveats

Completion quality is variable, being good for simple lines, mostly good for simple functions, and only sometimes for complex functions. Larger models are significantly slower. The implementation is noted as inefficient, and packaging as a standalone Mac app is not yet implemented.